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O R I G I N A L P A P E R - P R O D U C T I O N E N G I N E E R I N G
Automating sandstone acidizing using a rule-based system
AbdAllah S. Ebrahim
Ali A. Garrouch
Haitham M. S. Lababidi
Received: 9 October 2013 / Accepted: 11 January 2014 / Published online: 4 February 2014
The Author(s) 2014. This article is published with open access at Springerlink.com
Abstract An expert system for automating sandstone
acidizing has been developed in this study. The systemconsists of six stages, which were built following an aci-
dizing logic structure that is presented in the form of
decision trees. The six stages consist of formation oil dis-
placement, formation water displacement, acetic acid, HCl
pre-flush, main acid, and over-flush stage. The acid blends
recommended by the system are damage-type specific, and
account for the compatibility between the injected acid and
the in situ crude in order to avoid formation of asphaltene
sludge, or emulsions. The acidizing expert system has been
implemented as an online web-based application. Appli-
cability of this expert system to acidizing design has been
illustrated using three documented actual field cases
spanning the Niger Delta region, Algyo Oil field in Hun-
gary, and the Dulang oil field in Malaysia. For Niger Delta
field and the Algyo field cases the expert system produced
an optimal main acid job design with recommended pre-
and post-flushes that are in perfect agreement with suc-
cessful field treatment. For the Dulang oil field, in actual
practice, an organic clay acid was injected for removing
problems of fines migration in a reservoir that has a high
calcite content, with a moderate amount of feldspar and
chlorite clay. The acidizing expert system recommended a
chelant-based acid, which is a recent innovation that is
considered a more cost-effective acid solution for
dissolving fines in presence of calcite and other sensitive
clay minerals.
Keywords Acidizing Sandstone Expert system
Introduction
The selection of an appropriate acid type, concentration
and volume needed to be injected along with the required
additives and their concentrations for various temperature
and mineralogical environments can be a very perplexing
task. Part of this problem stems from the complex and
heterogeneous nature of most sandstone rocks. In addition,
the interactions between the many different mineral species
and the injected acid depend not only on their chemical
compositions but also on temperature, and on surface
morphology (Boyer1983).
Sandstone formations are challenging to acidize because
of the presence of alumino-silicates such as clays, zeolites,
and feldspars, which may lead to unwanted precipitates in
contact with mud acids as a result of secondary and tertiary
reaction products. For instance, smectite and mixed layer
clays are unstable in HCl at temperatures of approximately
150 F. Chlorite is unstable in presence of HCl at tem-
peratures above 125 F. When contacted with HCl, the clay
structure may disintegrate, releasing iron which may pre-
cipitate in presence of HCl acid (Rae and Di Lullo 2003).
Therefore, formations with high levels of chlorite respond
best to acid formulations based on acetic acid rather than
hydrochloric acid, since the former limits iron liberation
and thereby reduces the risk of precipitates from iron
reaction products (Nasr-El-Din and Al-Humaidan 2001;
Hashem et al. 1999). In formations with high levels of
feldspar ([20 %), a common practice is to limit the
A. S. Ebrahim A. A. Garrouch (&)
Petroleum Engineering Department, Kuwait University,
P.O. Box 5969, 13060 Safat, Kuwait
e-mail: [email protected]
H. M. S. Lababidi
Chemical Engineering Department, Kuwait University,
P.O. Box 5969, 13060 Safat, Kuwait
1 3
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DOI 10.1007/s13202-014-0104-3
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strength of HF acid stages to reduce the formation of
complex fluorosilicate precipitates and other species that
would result from excessive dissolution of the mineral by
stronger acid (Coulter and Jennings1997).
Illite clays are troublesome when using HF acid due to
the presence of potassium in this clay structure. When
dissolved, the potassium is readily available to react with
the HF-alumino-silicate reaction products, forming theinsoluble potassium hexa-fluosilicate. Illite is unstable in
HCl at temperatures above approximately 150 F. Kao-
linite clay is considered the most detrimental from a
migration standpoint (Coulter and Jennings 1997). It
becomes unstable in HCl only at higher temperatures
(greater than *200 F).
Zeolites are secondary minerals in the form of hydrated
silicates of aluminum, calcium, sodium, and potassium.
They are occasionally found in sedimentary rocks, with the
most common form being analcite (analcime). The signif-
icance of zeolites is that they either decompose and/or
gelatinize in hydrochloric acid at temperatures aboveapproximately 75 F (Coulter and Jennings1997).
The reasons for the disparity between the successful
sandstone matrix acidizing jobs and those treatments that
were unsuccessful may be grouped as follows:
a. Poor candidate selection
b. Lack of mineralogical information
c. Wrong acid design (strength, volume, etc.)
d. Use of inappropriate acid additives
e. Insufficient iron control
f. Use of contaminated/dirty fluids or neglecting to pickle
tubing stringg. Improper placement of acid (e.g., lack of diversion,
plugged perforations)
h. Long shut-in time without recovering injected fluids.
A long residence time of the injected fluids in the res-
ervoir causes formation of insoluble precipitates and for-
mation of very stable emulsions in the near well-bore
region (Barker et al. 2007). Acid formulation requires
careful mineralogical analysis of core samples. For
instance, a sandstone formation containing authigenic iron
chlorite clay within its pore spaces, even with low volume
fraction, may not respond to treatment with HF in any
concentration, and can be detrimental in response to HCl
treatment as well (Nwoke et al. 2004). On the other hand,
traditional guidelines, based on bulk mineralogy, might
suggest treatment with mild or moderate strength HF for
low total chlorite content regardless of the clay distribution
in the pore space (Coulter and Jennings 1997).
A large number of interacting variables come into play
for the selection of appropriate acid design job. For some
conditions, the design solution may not be a unique solu-
tion. However, there are many inappropriate design
solutions that could be formulated, if individuals super-
vising these jobs do not pay careful attention to the intri-
cate interactions between the rock, the injected acid, and
the in situ fluids.
In the last 20 years, few publications have emerged
related to expert system development for designing sand-
stone acidizing (Blackburn et al. 1990; Chiu et al. 1992;
Domelon et al. 1992; Nitters et al. 2000; Xiong and Hol-ditch 1994). Both Domelon et al. (1992) and Xiong and
Holditch (1994) introduced robust rule-based systems for
acid fluid selection that are damage-type specific, and
depend primarily on the formation mineralogy and the
produced fluid composition. Bartko et al. (1996) developed
an integrated matrix stimulation model that aids in diag-
nosing the formation damage type, optimizes injected acid
type, and provides a pressure-skin response of the acid
treatment. In all of these systems, however, the acid fluid
selection follows guidelines that do not reflect the recent
technological advances in acidizing blends formulations,
such as phosphonic acid blends and acid chelating blends.Instead, the acid selection is primarily based on mud acids,
organic acids, and clay acids. In addition, these rule-based
systems ignored the clay distribution in the rock, and were
rather forgiving with respect to the crudeacid interaction.
This research aims at the development and implemen-
tation of a Web-based acidizing expert system that
accounts for (i) the mineral distribution in the rock, (ii)
compatibility of the injected acid with the in situ crude, (iii)
reservoir temperature, (iv) compatibility of the injected
acid with the reservoir mineralogical composition, and (vi)
for the damage type. The base-fluid selection in our system
will depend primarily on recent novel acid blends intro-
duced in the industry that proved useful in preventing a
number of secondary and tertiary reaction products asso-
ciated with the use of regular mud acids. The remainder of
the manuscript reports the development of the reasoning
logic of the sandstone acidizing expert system, as well as
the implementation of the expert system.
Acidizing decision trees
The knowledge and reasoning logic incorporated in thesandstone acidizing expert system take into account input
data such as rock mineralogy, clay type and distribution,
reservoir temperature, and formation fluidsacid compati-
bility. The treatment design is constructed following a
sandstone acidizing structure that includes the following
stages:
Stage 1 Formation oil displacement
Stage 2 Formation water displacement
Stage 3 Acetic acid pre-flush
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Stage 4 HCl pre-flush
Stage 5 Main acid injection
Stage 6 Over-flush
In this structure, the pre-flush is no longer a single-stage,
and may be stretched to multiple stages. In fact, individual
Stages 14, or a combination of these may make-up the
needed pre-flush stage, depending on the conditions of therock after drilling, its mineralogy, presence of organic
deposits, the formation water salinity, and the calcite
content. The acidizing treatment design undertaken by
Stages 16 is based on knowledge and experience extracted
from human experts and arranged in hierarchical (tree)
forms, which may be termed as decision trees which are
used in the current work to represent the acquired knowl-
edge and reasoning logic. A decision tree is a tree-like
decision support tool that uses a graph to convey decisions
and their possible consequences (Adamo 1980; Yuan and
Shaw 1995). It contains mainly three types of nodes:
decision nodes associated with conditions and statementsto support decision making, chance nodes to represent
derived decisions and events that are likely to occur, and
end nodes corresponding to situations and end goals to be
obtained. Decision trees are essential to understand and
follow up the logic of the system. They may be considered
as references and means of communication between the
expert and the developer. Moreover, they facilitate main-
taining, checking, modifying and extending the knowledge
and logic of the system.
Stage 1 addresses the cleaning of whole mud losses that
take place during the drilling phase. It is also concerned
with the cleaning of organic deposits in the formation. Thedecision tree for Stage 1 is given in Fig. 1. This stage pre-
pares the surfaces for the main treatment fluids. Hydrocar-
bon solvents are used to clean oil films and paraffin deposits
so that the main acid systems can contact the mineral sur-
faces. In Stage 2, brine containing ammonium chloride is
used to help remove and dilute acid-incompatible species,
such as potassium or calcium (Fig. 2). This process helps
avoid precipitation of some of the most detrimental pre-
cipitates produced in sandstone acidizing such as sodium
and potassium fluorosilicates. Ammonium chloride is also
used to condition the clays that might come in contact with
injected acids. The lower the formation water salinity is, thehigher the concentration of ammonium chloride is needed to
suppress the electrical double layer of clays. A linear rela-
tionship between ammonium chloride concentration and
water salinity has been adopted in this study. This is
inspired from estimates of the critical salt concentration
needed for clay stability (Schechter 1992). The boundary
points of this linear relationship consist of 8 % ammonium
chloride solution for 0.1 % water salinity, and 3 %
ammonium chloride solution for 5 % water salinity, or
greater. Stage 3 is reserved for formations that bear iron-
rich minerals, or iron-rich clays like chlorite (Fig.3).
Injection of HCl acid in these rocks is likely to precipitate
iron scales when iron-rich minerals are present (Coulter and
Jennings1997). In order to alleviate these problems, HCl is
substituted with acetic acid when the volume fraction of
these authigenic species is [6 % (Fig. 3). Acetic acid
lessens the risk of precipitates from iron reaction products.Compatibility of the main acid with formation fluids is
another consideration for pre-flushes. A number of crudes
may sludge in contact with certain acidic mixtures (Houchin
et al. 1990). These situations may require buffering acetic
acid as a pre-flush. In the absence of sludge and emulsion
problems, HCl pre-flush (Stage 4) in sandstone acidizing
becomes extremely important. The function of an HCl pre-
flush is to remove the bacteria that may exist with injection
wells, calcareous material growth in the pore system, or to
remove CaCO3 inorganic scale deposits, and the calcite
cementing material that may precipitate calcium fluoride
deposits in contact with HF acid of the main stage. HCl pre-flush also reduces the potential precipitation of insoluble or
slightly soluble reaction products like calcium fluoride, and
sodium and potassium hexafluorosilicates. The decision tree
for HCl pre-flush is shown in Fig.4.
Selection of the main acid treatment as a function of
rock mineralogy for removing fines migration problems
is represented in the decision tree shown in Fig.5.
The selection process follows the subsequent general
guidelines:
1. Conventional mud acids are used only in very special
circumstances and in general in low concentrations inorder to avoid the precipitation of many damaging
reactants, maintain formation integrity, and dissolve
any fines. Indeed, mud acid is restricted for treating
clay-clean formations at relatively low temperatures
with insignificant amounts of calcite, Feldspar, or
zeolite.
2. Organic acids are recommended for clean rocks that
bear a minimum amount of clays, but at relatively high
temperatures. Shaly rocks that are free of either calcite
or zeolites are treated with a clay acid.
3. Shaly rocks that bear significant chlorite presence are
treated with a clay acid, a reducing agent likeerythorbic acid, and a sequestering agent like EDTA.
4. Shaly rocks that bear significant chlorite, feldspar or
zeolites but with low calcite content are treated with a
phosphonic acid blend.
5. Formations with low clay content, at high temperatures
and with significant calcite content are treated with
acid chelating blends.
6. Fracture option is reserved for reservoirs that bear
highly non-paraffinic crudes that may develop rigid
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emulsions or asphaltene sludge upon contact with HCl
(Houchin et al.1990).
The purpose of the over-flush (Stage 6) is to eliminate
damage in the near-wellbore area caused by the precipi-
tation potential of the spent acid of the main fluid stage
(Fig.6). This is accomplished by displacing the main fluid
stage more than 34 ft away from the wellbore, and by
diluting the portion of the main fluid stage that is not dis-
placed. Over-flush fluids are chosen carefully to be aqueous
based, and have dilution potential for the spent acid. The
fluids used in the over-flush stage must be miscible with theprevious stages.
The final design is reduced to include only necessary
steps, though. A great emphasis is placed on Stages 4 and
5. In other words, Stage 1 is skipped if none of the diamond
conditions of decision tree shown in Fig. 1 are satisfied.
Stage 3 is skipped, if the rock has no zeolite material, iron
minerals, illite, chlorite, or mixed layer clays. Stage 4 is
skipped if the conditions of the OR-Test S4-1, shown in
Fig.7a, are not satisfied. Decision trees that match the
damage types and initial formation conditions for the six
treatment stages have been constructed in this study. These
decision trees make the logical foundation for the expert
system decision-making process (Figs.1, 2, 3, 4, 5, 6),
covering the following damage types:
1. Particle damage from drilling and completion.
2. Fines migration.
3. Calcium carbonate scale
4. Hydroxide scale (Mg(OH)2, Ca(OH)2)5. Iron scales (FeS, Fe2O3, FeCO3)
6. Polymer residue from drilling or secondary recovery
7. Bacterial infestation (injection wells)
In an effort to extend the life of the acid treatment and
improve the outcome of the acidizing job, alternative acid
blends to the conventional HCl-HF acid systems were set
as part of the remedies employed in the decision trees
(Fig.5). In one of the improved chemistry systems, HCl is
replaced with a phosphonic acid complex which has five
available hydrogen ions that dissociate at different stoi-
chiometric conditions. For this reason, the phosphonic acid
complex is referred to as a five-hydrogen (HV) complex(Nwoke et al.2004; Uchendu and Nwoke2004; Rae and Di
Lullo 2007). The HV acid reacts with ammonium bifluo-
ride (NH4HF2) or with ammonium fluoride to produce HF
acid. In order to produce a 1 % HF acid solution, 20 gal of
HV acid per 1,000 gal of water are required to react with
approximately 123 lbm of NH4HF2 (Uchendu et al. 2006).
This self-generating reaction of HF acid reduces the rate at
which the acid system reacts, and therefore, allows an
increased depth of penetration of live HF acid into the
Stage 1: Formation Damage Displacement
Are there whole
mud losses?
(water-based mud)
Start
Was oil based
mud used?
Are there any
organic deposits?
Inject a mixture of diesel and
toluene at 75:25 ratios.
Soak overnight and flow back
GOTO
Inject an organic solvent
xylene or toluene/crystal
modifiers.
Inject a mutualsolvent.
GOTO
Are there any
organic deposits?
No action needed
No
No
No
Yes
YesYes
Yes
No
Fig. 1 Decision tree for
formation oil displacement
Stage 1
Stage 2: Formation Water Displacement
Inject water with ammonium
chloride (NH4Cl) at
concentrations between 3%
and 8% depending on the
formation water salinity
Fig. 2 Decision tree for water displacementStage 2
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Stage 3: Acetic Acid Pre-Flush
Are there iron
compounds in the
formation, sum > 6%?
- Pyrite, or- Siderite, or
- Hemetite, or
- Magnetite, or- Antecerite
Start
Inject acetic acid according tocalcite content:
CaCO3Acetic acid
volume (gal/ft)
1 5% 25
5 10% 50
10 15% 75
15 20% 100
No action needed
No Yes
No
Are there clays in theformation, sum > 6%?
- Chlorite, or
- Mixed layer, or- Illite
Are there zeolites in theformation, sum > 2%?
- Analcime, or- Natrolite
Yes
Yes
No
Fig. 3 Decision tree for acetic
acid pre-flushStage 3
Stage 4: HCl Pre-Flush
Damage Types: 3, 4, 5, 6 & 7
OR Test S4-1
Start
Acetic acid 10% +
Phosphonic acid-based
system + EDTA
No
Yes
No action needed
High sludge/
High Emulsionpotential?
OR Test S4-2
No
Examine
fracturing option
Yes
Zeolites > 2% Zeolites > 2%YesYes
Temperature
200F
HCl 10% + Erythorbic
acid 10% + Acidic
chelant based fluid
Feldspar exists?
HCl 10% + Erythorbic
acid + Fluoboric acid +
EDTA
HCl 10% + Erythorbic
acid + EDTA
Acetic acid 10% +
Erythorbic acid +
Fluoboric acid + EDTA
HCl 3% + Erythorbic
acid + Fluoboric acid +
EDTA
Yes
NoYes
No
No
YesNo
Feldspar exists?
No
No
Yes No
Temperature
200F
HCl 3% + Acetic
acid 10% + Acidic
chelant based fluid
Yes
Fig. 4 Decision tree for HCl
acid pre-flushStage 4
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Sandstone
naturally fractured
with partial calcite
filling
OR
OR Test S4-1
Bacteria damage or
polymer residueCalcite content
> 6%
Authigenic
chlorite
OR
Crystal growthchlorite
Crystal growthFeldspar
Authigenic ironrich minerals
Detritalchlorite > 4%
DetritalFeldspa > 4%
Detrital ironrich minirals
> 4%
AuthigenicFeldspar
OR Test S4-2
Existence of
(a)
(b)
OR
Authigenic
Feldspar
OR Test S5-1
Existence of
Crystal growth
Feldspar
Crystal growth
Illite
Detrital
Feldspa > 10%
Authigenic
Illite
(c)
Fig. 7 a Logical OR Test S4-
1 used in Stage 4 b Logical
OR Test S4-2 used in Stages
4, 5 and 6 c Logical OR Test
S5-1 used in Stages 5 and 6
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formation. This slow reaction reduces the risk of formation
deconsolidation in the near-wellbore area, unlike the con-
ventional HF treatment which may deconsolidate the for-
mation in the near-wellbore region.
In addition, the reaction of the HV:HF system with clays
forms a thin aluminum silicate phosphonate coating on the
clay. This coating, in return, prevents further spending of
the HF acid and reduces the reaction rate on high surfacearea clays. As a consequence, the volume of silica material
that can be dissolved is increased, causing further
improvement to the near-wellbore permeability (Obadare
et al. 2006). The production of ammonium phosphonate
salt, while HF is evolved from the HV acid, eliminates the
generation of insoluble precipitates as the pH of the spent
acid system rises. This is a common problem with con-
ventional HF acid systems. Furthermore, the rock substrate
is conditioned to be water wet with this HV:HF chemistry.
Water-wet condition improves the acid contact with the
targeted alumino-silicate material. Precipitation of fluo-
rosilicates, hexafluorosilicates, alumino-fluorosilicates,iron compounds and calcium fluoride, commonly generated
during acidizing with conventional HF are prevented as a
consequence of the strong chelating property of the HV:HF
acid system (Nwoke et al., 2004). These numerous prop-
erties of the HV:HF acid system are, indeed, the reason for
the improved success rate of acid jobs in several case
histories.
To satisfy the need for a longer-lasting stimulation effect
without the generation of unwanted second-reaction and
third-reaction precipitates in sensitive sandstone forma-
tions, the expert system deploys another blend referred to
as the acidic chelant-based blends (Urraca and Ferenc
2009; Rae and Di Lullo 2007; Nasr-El-Din et al. 2002,
2007). The use of these acidic chelant-based blends is
restricted to high temperatures formations, with relatively
high carbonate content and low clay content. The advan-
tages of these acid blends consist of their ability to:
Dissolve both calcium and alumino-silicates.
Prevent the possible precipitation of reaction by-
products by sequestering many of the metal ions
present in the aqueous solution: Ca2?, Fe2?, Al3? ions.
Treat formations with high calcite content.
Treat formations with high iron content. Treat formations with zeolite bearing minerals.
Expert system development
System implementation
Implementation of the acidizing expert system has been
achieved in five phases as shown in Fig. 8. The first phase
is knowledge acquisition in which knowledge is elicited
from the expert in the field. In this phase, the necessary
knowledge is built up progressively through a series of
consultation sessions between the domain expert and the
artificial intelligence specialist, the knowledge engineer.
Knowledge acquired during these sessions is recorded,refined and structured so that it could be used in the rea-
soning process.
The main task of the second phase is to arrange the
acquired knowledge in decision trees, which are considered
as the main communication tools between the domain
expert and the system developer. Decision trees prepared
for Stages 16 are shown in Figs. 1, 2, 3, 4, 5, 6, respec-
tively. Main development and coding of the Acidizing
Expert System are performed in phase three.
The software used in the implementation of the system
is Exsys Corvid, which is supported and licensed by Ex-
sys Inc. Corvid is an expert system development tool that
can be used to automate decision-making processes (Exsys
Inc. 2010). Expert system development in Corvid is
achieved using object structures, logic blocks, action
blocks and interactive Java-based tools for Web delivery.
The first step in phase three is to formulate the system by
identifying and defining the variables that will be used in the
reasoning process. Variables are either input variables that
are acquired through interaction with the user or decision
variables that are inferred and concluded by the reasoning
process. The next step in phase three is to represent the
knowledge, arranged as decision trees, in IFTHEN rules
format. The expert system is then constructed using the
development mode of Exsys Corvid. This includes defining
the variables, followed by building the questions and
defining the interaction with the user. The knowledge base
is then constructed by coding the IFTHEN rules taking into
account the inference mechanism to be used in deriving the
conclusions. During the development phase, interaction
with the user is performed using Java Applet.
The developed expert system is tested and validated in
stage four. Ideally, testing and validation are performed by
PhaseOne
Knowledge Acquisition
PhaseTwo
Decision Trees
PhaseThree
Expert System Development
PhaseFour
Validation
PhaseFive
System Delivery
Fig. 8 Expert system implementation phases
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exhaustive combinations of the values of input variables.
Using the decision trees, the reasoning process is followed
for each combination and the conclusions of the system are
validated against the end nodes of the tree. Actual valida-
tion of the developed system is then performed by running
a set of case studies that reflect practical applications.
System implementation is finally concluded in stage five
by delivering it to the end user. In Exsys Corvid, the
developed expert system may be delivered using Java
Applet or Servlet Runtime. The former is for standalone
applications, while the latter is for Web-based applications.
Reasoning structure
The structure of the acidizing expert system is shown in
Fig.9. Each stage in the hierarchy corresponds to a Logic
Fig. 9 Structure of the
acidizing expert system
Fig. 10 Command block for
the acidizing expert system
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Block consisting of rule sets specialized in modeling the
logic and deriving the required decisions. Moreover, each
stage corresponds to a decision tree representing the
knowledge elicited from the expert. Decision trees are
constructed in the previous section and illustrated inFigs.1,2, 3, 4,5, 6.
An acidizing consultation session starts by processing
the first three stages sequentially, followed by asking about
the type(s) of damage. Stages 1 and 2 are designed to treat
damage type 1. Stage 3 scrutinizes conditions that wave
HCl injections for preventing sludging, rigid emulsions,
and iron precipitates. The reasoning process is then direc-
ted to infer the rules associated to Stage 4 and/or Stage 5,
based on the selected damage type(s). Damage types 3, 4,
5, 6 and 7 are covered by the HCl pre-flush of Stage 4
(Fig.4). The reasoning process then proceeds to Stages 5
for deriving the recommendations for treatment of fines
migration (damage type 2, Fig. 5). Finally, the reasoning
process proceeds to Stage 6 which is concerned with thefluid selection for the post-flush (Fig. 6).
The reasoning protocol outlined above is defined in
Exsys Corvid using a Command Block, which is shown
in the Exsys Corvid Window capture shown in Fig. 10. The
statements listed in the command block are normally
executed sequentially. After displaying the title page
(TITLE statement), the system is directed to derive the
value of the Confidence variable [Stage_Two]. Conse-
quently, backward chaining will be invoked, which would
Fig. 11 Logic module
representing Stage 1
Fig. 12 IFTHEN
representation of a rule in Exsys
Corvid
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process the logic blocks for both Stage 1 and Stage 2.
The third statement/command is RESULTS, which displaysthe results or conclusions reached so far. This is followed
by a command to derive the Confidence variable
[Stage_Three].
The command block shown in Fig.10 results in dis-
playing intermediate conclusions after completing the
execution of each stage. Alternatively, if only final con-
clusions are required, then the command block would
simply include the following statements:
TITLE
DERIVE [Stage_Seven]
RESULTS
This would result in nesting the backward chainingreasoning back to the logic block for Stage 1, followed by
processing the logic blocks related to the stages down the
hierarchy (Fig. 9).
Rules are expressed in Exsys Corvid using Logic
Modules. They include a set of IFTHEN rules describing
a reasoning logic. An example logic module describing the
logic in Stage 1 is shown in Fig. 11. Moreover, the IF
THEN representation of the first rule of this logic module is
shown in Fig. 12.
Decisions derived by Stage 1 include:
a. Inject a mixture of diesel and toluene at 75:25 ratios.Soak overnight and flow back.
b. Inject an organic solvent xylene or toluene/crystal
modifiers.
c. Inject a mutual solvent.
d. No action needed.
The rule displayed in Fig. 12 uses two Static List
variables to check if there are whole mud losses and
any organic deposits. If the values of both variables are
Yes, then the rule concludes decisions (a) and (c).
Only one decision is derived from Stage 2 (see Fig.2). For
the acetic acid pre-flush stage (Stage 3), reasoning is mainlybased on the iron, clay and zeolite contents in the formation.
As shown in Fig. 3, two conclusions are possible for this
stage: No action needed if the formation doesnot have iron,
clay or zeolite, and Inject acetic acid if they exist. More-
over, the system suggests the volume of acetic acid, which is
determined based on the calcite (CaCO3) content, expressed
as a percentage out of the total rock bulk volume.
The reasoning logic for Stage 4 checks sludge and/or
emulsion potential logic shown in Fig.13. If the state-
ments in either one of the logical TRUE blocks are true,
then the sludge and/or emulsion potentials are inferred
as high. Another two logical tests needed for Stage 4reasoning are OR Test S4-1 and OR Test S4-2,
which are shown in Fig. 7 a and b, respectively. The
outcome of this stage is nine decisions, seven of which
are recommending the composition of the main acid to
be injected for the pre-flush stage. Asphaltene sludging
is likely to take place when a highly non-paraffinic
crude, with API gravity C27 and stock tank asphaltene
content is less or equal to 3 % by weight, is in contact
with HCl, or HCl/HF blends. Rigid emulsions, on the
other hand, would take place for the same conditions
when the crude is highly non-paraffinic with API gravity
B22 and stock tank asphaltene content is C4 % byweight (Houchin et al. 1990).
Acid formulation for Stage 5 is designed to treat spe-
cifically fines migration damage. The decision tree used in
developing the logic module for this stage is shown in
Fig.5. In addition to the two OR tests used in Stage 4
(Fig.7a, b), Stage 5 starts by checking one more OR test,
which is OR Test S5-1 shown in Fig. 7c. The outcome
of this stage is a recommendation on the type of main acid
to be used (see Fig. 5).
High sludge/
High Emulsion
potential?
AND
API 22STO
Asphaltenecontent 4%
Crude highly
non-paraffinic
AND
API 27STO
Asphaltenecontent 3%
Crude highly
non-paraffinic
OR
Fig. 13 Testing for sludge and/
or emulsion potential, used in
Stage 4
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The last logic module to process is Stage 6. As shown in
Fig.6, the reasoning process derives the recommendation
to use one of the four options for acid over-flush.
The acidizing expert system has been developed as a
web-based application. A snapshot of the main page of the
system is shown in Fig. 14. The same website hosts another
expert system for accessing formation damage, which is the
subject of another future publication. Sample results pagelisting the recommendations of an acidizing session is
shown in Fig. 15.
Acidizing system validation
Prior to starting a session in the acidizing expert system,
the following data should be prepared by the user:
1. Volume fractions of iron-rich minerals such as pyrite,
siderite, hematite, magnetite, and antecerite.
2. Volume fractions of chlorite, mixed layer clays and
illite, and Na-feldspar and K-feldspar.
3. Volume fractions of calcite and zeolites (analcime,
natrolite).
Fig. 14 Home page of the
online version of the acidizing
expert system
Fig. 15 Recommendations ofthe acidizing expert system for
the Niger Delta field case
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4. Whether the clays, iron-rich minerals, calcite and
feldspars exist as authigenic, detrital or crystal
growth.
5. API gravity of crude, whether it is highly paraffinic
or non-paraffinic, and the stock tank weight percent
of asphaltenes.
6. The reservoir temperature and thickness.
7. Whether there were any whole mud losses during
drilling of the well.
8. Whether the studied well is an injection or a
production well.
9. Whether there is any bacteria growth on the wellbore
face.
10. Whether there were any polymer flooding performed
prior to the acidizing job.
11. Whether there are any natural fractures, and whether
there is any filling of these fractures by calcite
material.
12. The type of damage that is required to remove.
The acidizing expert system has been validated with a
number of field cases from oil fields around the world.
Three actual field cases will be discussed and reported in
this study. They include the Niger Delta region, Algyo Oil
field in Hungary, and the Dulang oil field in Malaysia.
Case no. 1: Niger Delta region
This case study is related to a low-pressure sandstone oil
producer well in the Niger Delta region of southern
Nigeria. Rock properties were gathered for a number of
layers of this reservoir (Obadare et al. 2006). A represen-tative mineralogical distribution used by the expert system
is displayed in Table 1. Permeability ranges from 100 to
5,000 mD. Clay constituents, composition and distribution
were mapped for the concerned layers with a reasonable
statistical accuracy. The crude has a 0.663 downhole spe-
cific gravity, and the reservoir temperature is 188 F. The
pay thickness is approximately 21.7 ft. The main concern
with this well is the high potential of gelatinous precipitates
that may form in presence of zeolites, feldspar, clay, and
fines migration material. In addition to the data given so
far, following are further information that answers the
queries of the system:
1. Existence of mud losses to the formation because the
well was drilled overbalanced with a water-based mud,
and the reservoir unit has a fairly high permeability
value.
2. A finite amount of polymer residue may have been left
in the formation, as a consequence of the lost
circulation material during drilling.
3. Crude is paraffinic.
4. Iron-rich minerals exist as authigenic.
5. Feldspar material exists as detrital.
6. Intermediate matrix treatment is needed because of the
high rock permeability.
For the given data and information, the acidizing expert
system recommended the following acid treatment (see
Fig.15):
Stage One: Formation oil displacement stage: inject a
mixture of diesel and toluene at 75:25 ratio. Soak overnight
and flow back. Inject a mutual solvent.
Stage Two: Formation water displacement stage: inject
water with ammonium chloride at concentrations between
3 and 8 %, depending on the formation water salinity.
Stage Three: Acetic acid pre-flush: no action is needed
for this stage.
Stage Four:HCl pre-flush: inject HCl 3 % ? Fluoboric
acid ? Erythorbic acid ? EDTA.
Stage Five: Main acid stage: inject phosphonic acid.
Stage Six: Over-flush stage: inject 8 % NH4Cl.
As reported by Obadare et al. (2006), the actual treat-
ment used for the Niger Delta well consisted of a solventspearhead ? 10 % HCl pre-flush ? HF phosphonic acid
system ? 5 % HCl ? 3 % ammonium chloride containing
a clay stabilizer and a mutual solvent. This acid blend is in
perfect agreement with the blend recommended by the
acidizing expert system. The slight discrepancy is that the
expert system followed a conservative approach in adding
fluoboric acid, erythorbic acid and EDTA in the pre-flush
recommendation. In fact, fluoboric acid assures the disin-
tegration of any clay cementing material, erythorbic acid
Table 1 Mineralogical input data for the actual field cases used in
validating the acidizing expert system
Location Niger Delta Algyo-Ex1 Dulang
Depth (ft) 6,230 7,982 15,000
Quartz 73.2 45.0 51.9
K-Feldspar 13.6 16 5.2
Plagioclase (CalciumSodiumFeldspar)
4.1 0.0 2.4
Illite/smectite 0.7 0.0 10.9
Mica 0.0 2.0 0.0
Kaolinite 6.3 4.0 0.0
Chlorite 0.0 18 4.3
Dolomite 0.0 15 0.0
Calcite 0.0 7 15.0
Siderite 1.4 0.0 9.3
Pyrite 0.7 0.0 0.0
Hematite 0.0 0.0 0.0
Zeolite 0.7 0.0 1.0
Total 100.0 100.0 100.0
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6. Near-Wellbore treatment is needed because of the
insignificant mud losses.
The acidizing expert system recommended the follow-
ing treatment procedure:
Stage One:Formation oil displacement stage: No action
is needed.
Stage Two: Formation water displacement stage: injectwater with ammonium chloride at concentrations between
3 and 8 %, depending on the formation water salinity.
Stage Three: Acetic acid pre-flush: Inject 310 % acetic
acid, volume 100 gal/ft.
Stage Four: HCl pre-flush: inject HCl 3 %.
Stage Five: Main acid stage: inject acid chelant-based
fluid.
Stage Six: Over-flush stage: inject 8 % NH4Cl.
The actual treatment fluid, which was reported by Ali
et al. (2005), consisted of a 10 % acetic acid pre-flush, an
organic clay acid and an organic mud acid as the main acid.
The well showed an insignificant production improvementof approximately 100 bbl/day production. Even though
organic mud acid has the ability to dissolve fines at this high
reservoir temperature, the occurrence of secondary and
tertiary reactions in presence of moderate amounts of chlo-
rite and feldspar makes the use of mud acid a gamble.
Organic clay acid would be useful for removing problems of
fines migration at high reservoir temperature, provided that
the calcite content is low. However, at this combination of
high temperature, high calcite content, presence of moderate
amount of feldspar and chlorite in addition to fines migration
problem, a better main acid solution becomes the chelant-
based acid recommended by the acidizing expert system.
Conclusions
This manuscript documents the development of an expert
system for automating the design of sandstone acidizing.
The system has been implemented, tested and validated
with actual field data.
The acidizing system has been developed using revised
acidizing guidelines that are formation damage specific, and
are also specific to rock mineralogical composition and
distribution. Traditional guidelines have been modified withrespect to certain mineral sensitivities. Specifically, these
modifications included more explicit consideration for the
presence of acid-sensitive minerals such as zeolites, chlo-
ride, and feldspars, and their distribution in the rock matrix
and in the pore space. These guidelines have been also
augmented with respect to certain acid blends such as
phosphonic acids and acid chelant systems which are more
tolerant to temperature, calcium and zeolite presence, and to
clay sensitivity.
The treatment design approach, implemented in the aci-
dizing expert system, is developed following an acidizing
structure that includes guidelines prepared in the form of
decision trees for six stages, namely: (i) the formation oil
displacement stage, (ii) the formation water displacement
stage, (iii) the acetic acid stage, (iv) the HCl pre-flush stage,
(v) the main acid stage, and (vi) the over-flush stage.
Integration of rules honoring the compatibility betweenthe acid injected and the rock mineralogy, and fluids
present in the rock, yields an optimal acid job design with
recommended main acid volumes, pre- and post-flush flu-
ids. The acidizing system is only applicable for a reservoir
permeability not \10 mD for oil-bearing layers, and not
\1 mD for gas-bearing layers. This permeability cut-off,
at reasonable layer thickness, should provide oil and gas
production at profitable rates after damage removal. For
permeability values less than these cut-off values,
hydraulic fracturing becomes a viable option.
Acknowledgments The authors wish to thank Kuwait Oil CompanyManagement for the permission to publish this work. The authors are
grateful to Bader Al-Matar, Ali Afzal, Modhi Al-Ajmi and Huda Al-
Enizi from Kuwait Oil Company for their continued support and
advice on the project.
Open Access This article is distributed under the terms of the
Creative Commons Attribution License which permits any use, dis-
tribution, and reproduction in any medium, provided the original
author(s) and the source are credited.
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